amw_highway_notes

Highway Geometry

Grab highway shape geometry

# map it
mapview(bay_highways_shp)

Grab Indoor/Outdoor Sensors

Grab all our sensors, both in our study and across the Bay (filtered to San Mateo County). Highways are merged via union for upcoming analysis.

mapview(all_27_sensors) + mapview(smc_highways)

Indoor/Outdoor Analysis within 1/2 mile of highways

Add a .5 mile buffer to highways to qualitatively compare air quality for sensors near and far from highways. .5 miles chosen arbitrarily.

mapview(smc_sensors_near_highway) + mapview(smc_highways)

add tags for upcoming visualizations (near highway/sensor type)

Visualize sensors near highway map

leaflet() %>% 
  addMapboxTiles(
    style_id = "light-v9",
    username = "mapbox"
  ) %>% 
  addPolylines(
    data = smc_highways,
    fillColor = "black", 
    fillOpacity = 1,
    color = "black", 
    weight = 2
  ) %>% 
  addPolygons(
    data = highway_buffer,
    fillColor = "yellow",
    fillOpacity = .2,
    color = "yellow",
    weight = .2
  ) %>% 
  addCircleMarkers(
    data = smc_sensors %>% filter(near_highway == "YES", type == "original"),
    fillColor = "green",
    fillOpacity = 1,
    color = "black",
    opacity = 1,
    radius = 6,
    weight = .8,
    group = "Original Sensor Near from Highway"
  ) %>% 
  addCircleMarkers(
    data = smc_sensors %>% filter(near_highway == "NO", type == "original"),
    fillColor = "red",
    fillOpacity = .5,
    color = "black",
    opacity = 1,
    radius = 6,
    weight = .2, 
    group = "Original Sensor Away from Highway"
  ) %>% 
  addCircleMarkers(
    data = all_27_sensors %>% filter(near_highway == "YES", type == "participant"),
    fillColor = "purple",
    fillOpacity = 1,
    color = "black",
    opacity = 1,
    radius = 6,
    weight = .8,
    group = "Participant Sensor Near Highway"
  ) %>%
  addCircleMarkers(
    data = all_27_sensors %>% filter(near_highway == "NO", type == "participant"),
    fillColor = "purple",
    fillOpacity = 1,
    color = "black",
    opacity = 1,
    radius = 6,
    weight = .2,
    group = "Participant Sensor Away Highway"
  ) %>% 
  addLayersControl(
    overlayGroups = c("Original Sensor Away from Highway",
                   "Original Sensor Near from Highway",
                   "Participant Sensor Near Highway",
                   "Participant Sensor Away Highway"),
    options = layersControlOptions(collapsed = FALSE)
  )

# mapshot(m, file = paste0(path, "highway_analysis.png"))

Binary AQI analysis

The plot below shows little difference between AQi in sensors near the highway or further away.

AQI_near_highway_plot <- 
  ggplot(data = SMC_summary,
         aes(x = near_highway,
             y = average_AQI)) +
  geom_col() + 
  scale_fill_brewer(palette = "YlOrRd")

AQI_near_highway_plot

Improve analysis to measure AQI continuously

Use st_nearest to determine the distance from each sensor to the nearest highway. Below is a map that shows one sensor connected to its nearest highway.

nearest_distance <- 
  st_nearest_points(smc_sensors_AQI[1,],smc_highways) %>% 
  st_sf()

# visualize
leaflet() %>% 
  addMapboxTiles(
    style_id = "light-v9",
    username = "mapbox"
  ) %>% 
  addPolylines(
    data = smc_highways,
    fillColor = "black", 
    fillOpacity = 1,
    color = "black", 
    weight = 2
  ) %>% 
  addPolygons(
    data = highway_buffer,
    fillColor = "yellow",
    fillOpacity = .2,
    color = "yellow",
    weight = .2
  ) %>% 
  addCircleMarkers(
    data = smc_sensors_AQI %>% filter(near_highway == "YES"),
    fillColor = "green",
    fillOpacity = 1,
    color = "black",
    opacity = 1,
    radius = 6,
    weight = .8,
    group = "Original Sensor Near from Highway"
  ) %>% 
  addCircleMarkers(
    data = smc_sensors_AQI %>% filter(near_highway == "NO"),
    fillColor = "red",
    fillOpacity = .5,
    color = "black",
    opacity = 1,
    radius = 6,
    weight = .2, 
    group = "Original Sensor Away from Highway"
  ) %>% 
  addPolylines(
    data = nearest_distance %>% 
      st_transform(4326),
    color = "blue",
    weight = .5
  ) %>% 
  addLayersControl(
    overlayGroups = c("Original Sensor Away from Highway",
                   "Original Sensor Near from Highway"),
    options = layersControlOptions(collapsed = FALSE)
  )

# distance_to_hwy <- 
#   st_nearest_points(smc_sensors_AQI[3,ncol(smc_sensors_AQI)],smc_highways) %>% 
#   st_length() %>% 
#   as.numeric()

Regression analysis to see relationship between AQI and distance

First, plotting AQI relative to distance from highway, then a linear regression summary showing that according to our model, distance from highway is a poor predictor of AQI.

hwy_AQI_distance <- ggplot(smc_sensors_AQI,
                           aes(`d_to_hwy (m)`,AQI)
                           ) +
  geom_point() +
  geom_smooth()

hwy_AQI_distance

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

# linear regression formula
fit <- lm(AQI ~ `d_to_hwy (m)`, data = smc_sensors_AQI)
summary(fit)

## 
## Call:
## lm(formula = AQI ~ `d_to_hwy (m)`, data = smc_sensors_AQI)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -26.345  -3.345  -1.240   3.643  71.679 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     1.536e+01  2.798e-01  54.895   <2e-16 ***
## `d_to_hwy (m)` -4.175e-05  6.983e-05  -0.598     0.55    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.119 on 1040 degrees of freedom
## Multiple R-squared:  0.0003435,  Adjusted R-squared:  -0.0006177 
## F-statistic: 0.3574 on 1 and 1040 DF,  p-value: 0.5501

For every meter increase in distance to highway, our model predicts a -4.175 decrease in AQI.